AIRPNet: Adaptive Image Restoration With Privacy Protection in Steganographic Domain.

Abstract

Cloud-based third-party multimedia services have become increasingly popular in last decade, however, they pose serious threats to users' privacy. To address this issue, in this paper, we propose a novel Adaptive Image Restoration network with Privacy protection, namely AIRPNet, which first attempts to perform image restoration in steganographic domain. Compared with existing methods, our method has significant advantages in invisibility, security and flexibility. Specifically, we first propose a wavelet lifting-based Adaptive Invertible Hiding (AIH) module to conceal the low-quality (LQ) secret image into a stego image. Then, instead of performing single type of restoration on the secret image, an adaptive secure restoration (ASR) module is developed to deal with multiple image degradations on the stego image. Finally, a high-quality (HQ) secret image can be extracted from the restored stego image. Here, since the secret image remains hidden throughout the whole image restoration process, the privacy of users can be greatly protected. The framework can be flexibly extended to multiple image restoration, which can restore multiple secret images from the same stego image. Experimental results on various datasets demonstrate that our AIRPNet outperforms existing methods in terms of restoration accuracy, invisibility and security on different image restoration tasks.